Abstract
Nowadays deep learning has been intensively in spotlight owing to its great victories at major competitions, which undeservedly pushed ‘shallow’ machine learning methods, relatively naive/handy algorithms commonly used by industrial engineers, to the background in spite of their facilities such as small requisite amount of time/dataset for training. We, with a practical point of view, utilized shallow learning algorithms to construct a learning pipeline such that operators can utilize machine learning without any special knowledge, expensive computation environment, and a large amount of labelled data. The proposed pipeline automates a whole classification process, namely feature-selection, weighting features and the selection of the most suitable classifier with optimized hyperparameters. The configuration facilitates particle swarm optimization, one of well-known metaheuristic algorithms for the sake of generally fast and fine optimization, which enables us not only to optimize (hyper)parameters but also to determine appropriate features/classifier to the problem, which has conventionally been a priori based on domain knowledge and remained untouched or dealt with naïve algorithms such as grid search. Through experiments with the MNIST and CIFAR-10 datasets, common datasets in computer vision field for character recognition and object recognition problems respectively, our automated learning approach provides high performance considering its simple setting (i.e. non-specialized setting depending on dataset), small amount of training data, and practical learning time. Moreover, compared to deep learning the performance stays robust without almost any modification even with a remote sensing object recognition problem, which in turn indicates that there is a high possibility that our approach contributes to general classification problems.
Highlights
In recent years, deep learning has become a prominent class of methods for complex learning tasks including remote sensing (Krizhevsky et al, 2012; Basu et al, 2015)
Nowadays deep learning has been intensively in spotlight owing to its great victories at major competitions, which undeservedly pushed ‘shallow’ machine learning methods, relatively naive/handy algorithms commonly used by industrial engineers, to the background in spite of their facilities such as small requisite amount of time/dataset for training
Compared to deep learning the performance stays robust without almost any modification even with a remote sensing object recognition problem, which in turn indicates that there is a high possibility that our approach contributes to general classification problems
Summary
Deep learning has become a prominent class of methods for complex learning tasks including remote sensing (Krizhevsky et al, 2012; Basu et al, 2015). The key advantage of deep learning is that it can automatically learn suitable data representations, principally enabling these methods to surpass the performance of traditional approaches, which in turn requires a lot of labelled data and computational resources to train such flexible models. Traditional remote sensing approaches combine feature generators that are defined before the machine learning phase with shallow classifiers. Since these approaches essentially impose prior knowledge, they considerably reduce the learning requirements, such as the amount of labelled data and the computation resources, compared to deep learning. To facilitate rapid prototyping on novel data sets, we enhanced these traditional learning pipelines by investigating the potential of jointly determining feature generators, their corresponding weights and subsequent classifiers in a fully automated way
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